

'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_UpperCAmelCase  :		Union[str, Any]  =		logging.get_logger(__name__)

_UpperCAmelCase  :		int  =		{
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       'gpt_neo'
 UpperCamelCase__     =       ['past_key_values']
 UpperCamelCase__     =       {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







 def __init__(       self  , snake_case_=5_02_57  , snake_case_=20_48  , snake_case_=20_48  , snake_case_=24  , snake_case_=[[["global", "local"], 12]]  , snake_case_=16  , snake_case_=None  , snake_case_=2_56  , snake_case_="gelu_new"  , snake_case_=0.0  , snake_case_=0.0  , snake_case_=0.0  , snake_case_=0.1  , snake_case_=1E-5  , snake_case_=0.02  , snake_case_=True  , snake_case_=5_02_56  , snake_case_=5_02_56  , **snake_case_  , ):
   lowercase									=vocab_size
   lowercase									=max_position_embeddings
   lowercase									=hidden_size
   lowercase									=num_layers
   lowercase									=num_heads
   lowercase									=intermediate_size
   lowercase									=window_size
   lowercase									=activation_function
   lowercase									=resid_dropout
   lowercase									=embed_dropout
   lowercase									=attention_dropout
   lowercase									=classifier_dropout
   lowercase									=layer_norm_epsilon
   lowercase									=initializer_range
   lowercase									=use_cache

   lowercase									=bos_token_id
   lowercase									=eos_token_id

   lowercase									=attention_types
   lowercase									=self.expand_attention_types_params(snake_case_   )

   if len(self.attention_layers   ) != self.num_layers:
     raise ValueError(
         '''Configuration for convolutional module is incorrect. '''
         '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
         f'but is `len(config.attention_layers) = {len(self.attention_layers   )}`, '
         f'`config.num_layers = {self.num_layers}`. '
         '''`config.attention_layers` is prepared using `config.attention_types`. '''
         '''Please verify the value of `config.attention_types` argument.'''   )

   super().__init__(bos_token_id=snake_case_  , eos_token_id=snake_case_  , **snake_case_   )







 @staticmethod
 def 						_A(       snake_case_   ):
   lowercase									=[]
   for item in attention_types:
     for _ in range(item[1]   ):
       attentions.extend(item[0]   )
   return attentions




def 	UpperCamelCase   (       lowercase_						:    List[str]     ,					lowercase_						:    str     ,					lowercase_						:    str     ,					lowercase_						:    Optional[int]	)		->	str:




  '''simple docstring'''



  import torch

  lowercase									=input.size()
  lowercase									=len(lowercase_	)
  lowercase									=shape[dimension]

  lowercase									=torch.arange(0     ,					lowercase_     ,					lowercase_	)
  lowercase									=torch.div(sizedim - size     ,					lowercase_     ,					rounding_mode='''floor'''	) + 1
  lowercase									=torch.arange(lowercase_	) + low_indices[:min_length][:, None]

  lowercase									=[slice(lowercase_	)] * rank
  lowercase									=indices
  lowercase									=input[s]

  lowercase									=list(range(0     ,					rank + 1	)	)
  perm.append(perm.pop(dimension + 1	)	)

  return sliced.permute(lowercase_	)




def 	UpperCamelCase   (       lowercase_						:    Optional[int]     ,					lowercase_						:    Union[str, Any]	)		->	List[str]:




  '''simple docstring'''



  import torch

  lowercase									=torch.arange(1     ,					lowercase_	)
  lowercase									=torch.remainder(lowercase_     ,					lowercase_	)
  lowercase									=remainders == 0
  lowercase									=candidates[divisor_indices]
  lowercase									=torch.max(lowercase_	)
  return largest_divisor, torch.div(lowercase_     ,					lowercase_     ,					rounding_mode='''floor'''	)




class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 @property
 def 						_A(       self   ):
   lowercase									=OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}   )
   if self.use_past:
     self.fill_with_past_key_values_(snake_case_  , direction='''inputs'''   )
     lowercase									={0: '''batch''', 1: '''past_sequence + sequence'''}
   else:
     lowercase									={0: '''batch''', 1: '''sequence'''}

   return common_inputs







 @property
 def 						_A(       self   ):
   return self._config.num_heads







 def 						_A(       self  , snake_case_  , snake_case_ = -1  , snake_case_ = -1  , snake_case_ = False  , snake_case_ = None  , ):
   lowercase									=super(snake_case_  , self   ).generate_dummy_inputs(
       snake_case_  , batch_size=snake_case_  , seq_length=snake_case_  , is_pair=snake_case_  , framework=snake_case_   )

   # We need to order the input in the way they appears in the forward()
   lowercase									=OrderedDict({'''input_ids''': common_inputs['''input_ids''']}   )

   # Need to add the past_keys
   if self.use_past:
     if not is_torch_available():
       raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
     else:
       import torch

       lowercase							, lowercase									=common_inputs['''input_ids'''].shape
       # Not using the same length for past_key_values
       lowercase									=seqlen + 2
       lowercase									=(
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       lowercase									=[
           (torch.zeros(snake_case_   ), torch.zeros(snake_case_   )) for _ in range(self.num_layers   )
       ]

   lowercase									=common_inputs['''attention_mask''']
   if self.use_past:
     lowercase									=ordered_inputs['''attention_mask'''].dtype
     lowercase									=torch.cat(
         [ordered_inputs['''attention_mask'''], torch.ones(snake_case_  , snake_case_  , dtype=snake_case_   )]  , dim=1   )

   return ordered_inputs







 @property
 def 						_A(       self   ):
   return 13








"""simple docstring"""


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
		from PIL import Image




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Optional[Any]      =  """Salesforce/blip-image-captioning-base"""
					lowercase_    :				Optional[int]      =  (
					    """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
					    """image to caption, and returns a text that contains the description in English."""
					)
					lowercase_    :				int      =  """image_captioner"""
					lowercase_    :				int      =  AutoModelForVisionaSeq

					lowercase_    :				List[str]      =  ["""image"""]
					lowercase_    :				int      =  ["""text"""]
					def __init__(							self							:	Any     , *UpperCAmelCase							:	str     , **UpperCAmelCase							:	List[str]   )	->       Union[str, Any]:
											requires_backends(self     , ['vision']   )
											super().__init__(*UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	"Image"   )	->       Union[str, Any]:
											return self.pre_processor(images=UpperCAmelCase     , return_tensors='pt'   )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	Optional[int]   )	->       Optional[int]:
											return self.model.generate(**UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	int   )	->       Dict:
											return self.pre_processor.batch_decode(UpperCAmelCase     , skip_special_tokens=UpperCAmelCase   )[0].strip()